TY - GEN
T1 - Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
AU - Cai, Wanling
AU - Chen, Li
N1 - Funding Information:
This work was partially supported by Hong Kong Baptist University IRCMS Project (IRCMS/19-20/D05). We also thank Ms. Yangyang Zheng for her assistance in annotating.
PY - 2020/7/7
Y1 - 2020/7/7
N2 - To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.
AB - To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.
KW - dialogue-based conversational recommender systems
KW - intent taxonomy
KW - user intent prediction
KW - user satisfaction prediction
UR - http://www.scopus.com/inward/record.url?scp=85089349003&partnerID=8YFLogxK
U2 - 10.1145/3340631.3394856
DO - 10.1145/3340631.3394856
M3 - Conference contribution
AN - SCOPUS:85089349003
T3 - UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
SP - 33
EP - 42
BT - UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery (ACM)
T2 - 28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Y2 - 14 July 2020 through 17 July 2020
ER -